7 citations · 18 across the 12 of their papers we have counts for
13 papers
RAGNet: Large-scale Reasoning-based Affordance Segmentation Benchmark towards General Grasping
Dongming Wu, Yanping Fu, Saike Huang +8
General robotic grasping systems require accurate object affordance perception in diverse open-world scenarios following human instructions. However, current studies suffer from th…
PADriver: Towards Personalized Autonomous Driving
Genghua Kou, Fan Jia, Weixin Mao +7
In this paper, we propose PADriver, a novel closed-loop framework for personalized autonomous driving (PAD). Built upon Multi-modal Large Language Model (MLLM), PADriver takes stre…
Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated video training data. In this paper, we propose Panacea+, a powerful and universally applicable framework…
Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?
Yifan Bai, Dongming Wu, Yingfei Liu +8
Rapid advancements in Autonomous Driving (AD) tasks turned a significant shift toward end-to-end fashion, particularly in the utilization of vision-language models (VLMs) that inte…
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
Binyuan Huang, Yuqing Wen, Yucheng Zhao +9
Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data…
Stream Query Denoising for Vectorized HD Map Construction
Shuo Wang, Fan Jia, Yingfei Liu +6
To enhance perception performance in complex and extensive scenarios within the realm of autonomous driving, there has been a noteworthy focus on temporal modeling, with a particul…
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated training data. In this paper, we propose Panacea, an innovative approach to generate panoramic and contr…
ADriver-I: A General World Model for Autonomous Driving
Fan Jia, Weixin Mao, Yingfei Liu +5
Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular desi…
VLM-Eval: A General Evaluation on Video Large Language Models
Shuailin Li, Yuang Zhang, Yucheng Zhao +4
Despite the rapid development of video Large Language Models (LLMs), a comprehensive evaluation is still absent. In this paper, we introduce a unified evaluation that encompasses m…
TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
Dongming Wu, Jiahao Chang, Fan Jia +3
Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving. It requires detecting road centerlines (lane) and traffic eleme…
Far3D: Expanding the Horizon for Surround-view 3D Object Detection
Xiaohui Jiang, Shuailin Li, Yingfei Liu +5
Recently 3D object detection from surround-view images has made notable advancements with its low deployment cost. However, most works have primarily focused on close perception ra…
The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge
Dongming Wu, Fan Jia, Jiahao Chang +7
We present the 1st-place solution of OpenLane Topology in Autonomous Driving Challenge. Considering that topology reasoning is based on centerline detection and traffic element det…
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
Junjie Yan, Yingfei Liu, Jianjian Sun +4
In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the im…